On deconvolution of distribution functions

Research output: Contribution to journalArticlepeer-review


The subject of this paper is the problem of nonparametric estimation of a continuous distribution function from observations with measurement errors. We study minimax complexity of this problem when unknown distribution has a density belonging to the Sobolev class, and the error density is ordinary smooth. We develop rate optimal estimators based on direct inversion of empirical characteristic function. We also derive minimax affine estimators of the distribution function which are given by an explicit convex optimization problem. Adaptive versions of these estimators are proposed, and some numerical results demonstrating good practical behavior of the developed procedures are presented.

Original languageEnglish
Pages (from-to)2477-2501
Number of pages25
JournalAnnals of Statistics
Issue number5
StatePublished - Oct 2011


  • Adaptive estimator
  • Deconvolution
  • Distribution function
  • Minimax risk
  • Rates of convergence

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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